An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem

As same with many evolutional algorithms, performance of simple PSO depends on its parameters, and it often suffers the problem of being trapped in local optima so as to cause premature convergence. In this paper, an improved particle swarm optimization with decline disturbance index (DDPSO), is pro...

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Vydáno v:Computers & operations research Ročník 45; s. 38 - 50
Hlavní autoři: Zhao, Fuqing, Tang, Jianxin, Wang, Junbiao, Jonrinaldi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.05.2014
Elsevier
Pergamon Press Inc
Témata:
Adaptive meta-Lamarckian strategy
Algorithms
Applied sciences
Computer simulation
Decision theory. Utility theory
Decline disturbance
Disturbances
Effectiveness studies
Exact sciences and technology
Expanded job shop scheduling problem
Genetic algorithms
Job shops
Local optimization
Markov analysis
Mathematical models
Mathematical problems
Multi-objective job shop scheduling problem
Operational research and scientific management
Operational research. Management science
Optimization
Optimization algorithms
Particle swarm optimization
Performance indices
Production scheduling
Scheduling, sequencing
Searching
Stochastic models
Swarm intelligence
Adaptive meta-Lamarckian strategy
Multi-objective job shop scheduling problem
Particle swarm optimization
Expanded job shop scheduling problem
Decline disturbance
Markov process
Local search
Search strategy
Probabilistic approach
Pareto optimum
Job shop
Multiobjective programming
Task scheduling
Global optimum
Scheduling
Stochastic process
Adaptive method
Global solution
Workload
Global local method
Trapping
Neighborhood search
Swarm intelligence
Robustness
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:As same with many evolutional algorithms, performance of simple PSO depends on its parameters, and it often suffers the problem of being trapped in local optima so as to cause premature convergence. In this paper, an improved particle swarm optimization with decline disturbance index (DDPSO), is proposed to improve the ability of particles to explore the global and local optimization solutions, and to reduce the probability of being trapped into the local optima. The correctness of the modification, which incorporated a decline disturbance index, was proved. The key question why the proposed method can reduce the probability of being trapped in local optima was answered. The modification improves the ability of particles to explore the global and local optimization solutions, and reduces the probability of being trapped into the local optima. Theoretical analysis, which is based on stochastic processes, proves that the trajectory of particle is a Markov processes and DDPSO algorithm converges to the global optimal solution with mean square merit. After the exploration based on DDPSO, neighborhood search strategy is used in a local search and an adaptive meta-Lamarckian strategy is employed to dynamically decide which neighborhood should be selected to stress exploitation in each generation. The multi-objective combination problems with DDPSO for finding the pareto front was presented under certain performance index. Simulation results and comparisons with typical algorithms show the effectiveness and robustness of the proposed DDPSO.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2013.11.019